Aug 2026· Journal of Circuits, Systems and Computers· 0 citations· 25 references
Computer Science
TL;DR
This paper presents a novel memory controller architecture and a RISC-V instruction set extension to optimize MLC NVM write operations by balancing speed and retention time, and introduces a fast-store instruction in RISC-V to increasing write performance while addressing retention limitations.
Abstract
Non-volatile memory (NVM) technologies, particularly Multi-Level Cell (MLC) NVMs, offer significant potential for increasing memory density. MLC NVMs provide a tradeoff between write latency and retention time, where faster writes/stores result in lower retention and slower writes yield higher retention. However, limited work has been done to validate and prototype NVM-based systems in hardware, leveraging this tradeoff at the system level.
In this paper, we present a novel memory controller architecture and a RISC-V instruction set extension to optimize MLC NVM write operations by balancing speed and retention time. Our custom NVM controller, built around a finite state machine with an AXI memory-mapped interface, efficiently manages read/write operations with enhanced burst transfers, minimizing latency. Additionally, we introduce a fast-store instruction in RISC-V to increasing write performance while addressing retention limitations. Further, we design a dedicated AXI slave peripheral that supports bit-significance-aware writes: critical bits (e.g., MSBs) are written using slower, high-retention writes, while non-critical bits (e.g., LSBs) use faster, low-retention writes to help enhance performance without compromising data reliability. These enhancements are implemented in hardware on an FPGA platform. Experimental results show that our controller reduces hardware overhead by 30% compared to conventional designs, and the fast-store instruction improves performance by over 7% for streaming workloads with less than 0.08% hardware overhead. The bit-wise AXI peripheral has a LUT utilization staying below 3.5% even for 64×64 matrices, and under 1% for 32×32 sizes, making it viable for integration into larger SoCs.
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